Abstract
The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.
Highlights
Reducing road traffic injuries is one of the targets of the 17 Goals that were established by all United Nations member states in 2015 as part of the 2030 Agenda for Sustainable Development [1]
These Accident database (ADB) databases containing environmental, accident type, occupant, and vehicle information were merged into the database of vehicle registrations (VRDB) in order to obtain a database with the characteristics of LTVs involved in traffic accidents, as well the latter corresponding data (DB LTVs)
The approach presented in this study allowed us to identify significant categorical variables related to driver severity by selecting a reduced number of variables
Summary
Reducing road traffic injuries is one of the targets of the 17 Goals that were established by all United Nations member states in 2015 as part of the 2030 Agenda for Sustainable Development [1]. Goal 3, target 3.6 (Good health and Well-Being) states that by 2020, the number of deaths and injuries caused by traffic accidents worldwide [1,2] should be reduced by half. The significant reduction in Spain between the years 2001 and 2018, decreasing from 135 to 39 deaths (the target is fewer than 37 by 2020) per million inhabitants, ranks it among the seven top safest countries in the European Union. This information was issued by the Directorate General of Traffic (DGT) of Spain and the WHO [5,6]. The continuous growth of goods logistics and passenger transport, as well as growing access restrictions for industrial vehicles entering city centers, especially
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